Work

Foundation model embeddings for quantitative tumor imaging biomarkers

Year: 2025

Type: preprint

Source: Research Square

DOI: 10.21203/rs.3.rs-6630446/v1

Authors: Suraj Pai, Ibrahim Hadzic, Andrey Fedorov, Raymond H. Mak, Hugo JWL Aerts

Institutions: Mass General Brigham, Brigham and Women's Hospital, Harvard University

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: European Commission, National Institutes of Health, National Cancer Institute, H2020 European Research Council

Awards: Deep Learning for Automated Quantification of Radiographic Tumor Phenotypes, Genotype and Imaging Phenotype Biomarkers in Lung Cancer, G5024937287, Quantitative Radiomics System Decoding the Tumor Phenotype, G9000000961, G9000027136

OpenAlex ID: W4410861880

Cites: 37

Cited by: 1

Related to: 10